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Binary classification is typically achieved by supervised learning methods. Nevertheless, it is also possible using unsupervised schemes. This paper describes a connectionist unsupervised approach to binary classification and compares its performance to that of its supervised counterpart. The approach consists of training an autoassociator to reconstruct the positive class of a domain at the output layer. After training, the autoassociator is used for classification, relying on the idea that if the network generalizes to a novel instance, then this instance must be positive, but that if generalization fails, then the instance must be negative. When tested on three real-world domains, the autoassociator proved more accurate at classification than its supervised counterpart, MLP, on two of these domains and as accurate on the third (Japkowicz, Myers, & Gluck, 1995). The paper seeks to generalize these results and concludes that, in addition to learning a concept in the absence of negative examples, 1) autoassociation is more efficient than MLP in multi-modal domains, and 2) it is more accurate than MLP in multi-modal domains for which the negative class creates a particularly strong need for specialization or the positive class creates a particularly weak need for specialization. In multi-modal domains for which the positive class creates a particularly strong need for specialization, on the other hand, MLP is more accurate than autoassociation.